* Corresponding Author
Estimating the contents of Chlorophyll, Nitrogen, and Yields on Rice through Sentinel-2 Vegetation Indices in Heterogeneous Land Management
https://doi.org/10.22146/ijg.87159 Yagus Wijayanto(1*), Mahardika Safitri(2), Ika Purnamasari(3), Subhan Arif Budiman(4), Tri Wahyu Saputra(5), Arthur FC Regar(6), Suci Ristiyana(7)
(1) Faculty of Agriculture, University of Jember, Jl Kalimantan Kamus Tegalboto, Jember, 68121, Indonesia.
(2) Faculty of Agriculture, University of Jember, Jl Kalimantan Kamus Tegalboto, Jember, 68121, Indonesia.
(3) Faculty of Agriculture, University of Jember, Jl Kalimantan Kamus Tegalboto, Jember, 68121, Indonesia.
(4) Faculty of Agriculture, University of Jember, Jl Kalimantan Kamus Tegalboto, Jember, 68121, Indonesia.
(5) Faculty of Agriculture, University of Jember, Jl Kalimantan Kamus Tegalboto, Jember, 68121, Indonesia.
(6) Faculty of Agriculture, University of Jember, Jl Kalimantan Kamus Tegalboto, Jember, 68121, Indonesia.
(7) Faculty of Agriculture, University of Jember, Jl Kalimantan Kamus Tegalboto, Jember, 68121, Indonesia.
(*) Corresponding Author
Abstract
Addressing the global food demand is an urgent priority for governments worldwide. Efficient and effective methods for gauging crop production are crucial. Relying solely on ground-based measurements proves inefficient and expensive, prompting exploration of remote sensing using vegetation indices as a viable alternative. This study sought to achieve three objectives: estimating chlorophyll content in paddy fields, evaluating leaf nitrogen content, and predicting yields. The investigation utilized Sentinel-2A satellite imagery, Soil Plant Analysis Development (SPAD) for chlorophyll measurement, and employed statistical and accuracy analyses. Findings revealed an increase in chlorophyll and leaf nitrogen content from the vegetative to maturity phases, followed by a decline at maturity. NDVI and GNDVI emerged as superior to SAVI and VARI for chlorophyll estimation, attributed to their spectral sensitivity. Likewise, nitrogen prediction showed similar trends, with NDVI and GNDVI exhibiting better RMSE values compared to SAVI and VARI, albeit marginally. However, yield prediction accuracy varied, with NDVI proving most accurate, followed by SAVI, VARI, and GNDVI, indicating the latter's reduced predictive precision due to nitrogen sensitivity. In scenarios where nitrogen is not the predominant yield-limiting factor, NDVI could outperform GNDVI in forecasting yield.
Received: 2023-07-22 Revised: 2024-04-18 Accepted: 2024-08-24 Published: 2024-10-10
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Ali, A. M., Abouelghar, M., Belal, A. A., Saleh, N., Yones, M., Selim, A. I., … Savin, I. (2022). Crop Yield Prediction Using Multi Sensors Remote Sensing (Review Article). Egyptian Journal of Remote Sensing and Space Science, 25(3), 711–716. https://doi.org/10.1016/j.ejrs.2022.04.006
Ali, S., Haider, H., Mamtaz, S., & Riaz, A. (2023). Nutrients and Crop Production. Current Research In: Agriculture and Farming, 4(2), 1–15. https://doi.org/10.18782/2582-7146.182
Amirhusin, B., Wihardjaka, A., Enggarini, W., Yulianingrum, H., Sisharmini, A., & Apriana, A. (2023). PERFORMANCE OF NITROGEN-USE EFFICIENT NERICA4 RICE LINES IN INDONESIAN RAIN-FED ECOSYSTEMS. Applied Ecology and Environmental Research, 21(2), 943–956. https://doi.org/10.15666/aeer/2102_943956
Bautista, A. S., Fita, D., Franch, B., Castiñeira-Ibáñez, S., Arizo, P., Sánchez-Torres, M. J., … Rubio, C. (2022). Crop monitoring strategy based on remote sensing data (Sentinel-2 and planet), study case in a rice field after applying glycinebetaine. Agronomy, 12(3), 1–23. https://doi.org/10.3390/agronomy12030708
Chowdhury, M., Khura, T. K., Upadhyay, P. K., Parray, R. A., Kushwaha, H. L., Singh, C., … Mani, I. (2024). Assessing vegetation indices and productivity across nitrogen gradients: a comparative study under transplanted and direct-seeded rice. Frontiers in Sustainable Food Systems, 8. https://doi.org/10.3389/fsufs.2024.1351414
Connor, M., de Guia, A. H., Pustika, A. B., Sudarmaji, Kobarsih, M., & Hellin, J. (2021). Rice farming in central java, indonesia—adoption of sustainable farming practices, impacts and implications. Agronomy, 11(5). https://doi.org/10.3390/agronomy11050881
Gim, H. J., Ho, C. H., Jeong, S., Kim, J., Feng, S., & Hayes, M. J. (2020). Improved mapping and change detection of the start of the crop growing season in the US Corn Belt from long-term AVHRR NDVI. Agricultural and Forest Meteorology, 294, 108143. https://doi.org/10.1016/J.AGRFORMET.2020.108143
Giovos, R., Tassopoulos, D., Kalivas, D., Lougkos, N., & Priovolou, A. (2021). Remote sensing vegetation indices in viticulture: a critical review. Agriculture (Switzerland), 11(5), 1–20. https://doi.org/10.3390/agriculture11050457
Gnyp, M. L., Miao, Y., Yuan, F., Ustin, S. L., Yu, K., Yao, Y., … Bareth, G. (2014). Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages. Field Crops Research, 155, 42–55. https://doi.org/10.1016/j.fcr.2013.09.023
Guérif, M., Houlès, V., & Baret, F. (2007). Remote sensing and detection of nitrogen status in crops. Application to precise nitrogen fertilization.
Harfian, I., Fadhilah, N., & Amalia, A. F. (2020). Teknik Penggunaan Drone dengan Sensor Kamera RGB dan Algoritma VARI untuk Mengidentifikasi Tingkat Stres Tanaman Jagung. Buletin Teknik Pertanian, 25(2), 85–88. Retrieved from https://www.researchgate.net/publication/359368410_Teknik_Penggunaan_Drone_Dengan_Sensor_Kamera_RGB_Dan_Algoritma_VARI_Untuk_Mengidentifikasi_Tingkat_Stres_Tanaman_Jagung
He, X., Zhu, H., Shi, A., & Wang, X. (2024). Optimizing Nitrogen Fertilizer Management Enhances Rice Yield, Dry Matter, and Nitrogen Use Efficiency. Agronomy, 14(5), 919. https://doi.org/10.3390/agronomy14050919
Hisham, N. H. B., Hashim, N., Saraf, N. M., & Talib, N. (2022). Monitoring of Rice Growth Phases Using Multi-Temporal Sentinel-2 Satellite Image. IOP Conference Series: Earth and Environmental Science, 1051(1), 1–13. https://doi.org/10.1088/1755-1315/1051/1/012021
Htun, A. M., Shamsuzzoha, M., & Ahamed, T. (2023). Rice yield prediction model using normalized vegetation and water indices from Sentinel-2A satellite imagery datasets. Asia-Pacific Journal of Regional Science, 7(2), 491–519. https://doi.org/10.1007/s41685-023-00299-2
Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6. https://doi.org/10.1007/s11676-020-01155-1
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X
Hussain, T., Hussain, N., Ahmed, M., Nualsri, C., & Duangpan, S. (2022). Impact of Nitrogen Application Rates on Upland Rice Performance, Planted under Varying Sowing Times. Sustainability (Switzerland), 14(4). https://doi.org/10.3390/su14041997
Irfan, M., Ahmed, Z. I., Khan, T. A., Ahmad, W., Akhtar, M. T., Iqbal, K., & Malik, M. N. (2018). Monitoring of Wheat and Rice Nitrogen Status by Remote Sensing. Journal of Experimental Agriculture International, 28(6), 1–13. https://doi.org/10.9734/jeai/2018/16566
Joshua, V., Priyadharson, S. M., & Kannadasan, R. (2021). Exploration of machine learning approaches for paddy yield prediction in eastern part of Tamilnadu. Agronomy, 11(10), 2068.
Kaya, Y., & Polat, N. (2023). A linear approach for wheat yield prediction by using different spectral vegetation indices. International Journal of Engineering and Geosciences, 8(1), 52–62. https://doi.org/10.26833/ijeg.1035037
Li, C., Li, H., Li, J., Lei, Y., Li, C., Manevski, K., & Shen, Y. (2019). Using NDVI percentiles to monitor real-time crop growth. Computers and Electronics in Agriculture, 162, 357–363. https://doi.org/10.1016/J.COMPAG.2019.04.026
M, G., Karegowda, A. G., R, N., & B, N. B. (2022). Exploring vegetative indices for yield prediction using Sentinel 2 data a study in a select Region of Karnataka. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 574–581. Retrieved from www.ijisae.org
Mandla, V. R. (2017). Comparative study of NDVI and SAVI vegetation indices in Anantapur District semi-arid areas. International Journal of Civil Engineering and Technology, 8(4), 559–566. Retrieved from http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=4
Manessa, M. D. M., Supriatna, & Sidik, I. P. A. (2023). Age Estimation of Paddy using Sentinel-2 Imagery: A Case Study in Java Island, Indonesia. Agrivita, 45(3), 456–466. https://doi.org/10.17503/agrivita.v41i0.3106
Meivel, S., & Maheswari, S. (2022). Monitoring of potato crops based on multispectral image feature extraction with vegetation indices. Multidimensional Systems and Signal Processing, 33(2), 683–709. https://doi.org/10.1007/s11045-021-00809-5
Nakano, H., Tanaka, R., Guan, S., & Ohdan, H. (2023). Predicting rice grain yield using normalized difference vegetation index from UAV and GreenSeeker. Crop and Environment, 2(2), 59–65. https://doi.org/10.1016/j.crope.2023.03.001
Nuthammachot, N., & Stratoulias, D. (2023). EXPLORING SENTINEL-2 SATELLITE IMAGERY-BASED VEGETATION INDICES FOR CLASSIFYING HEALTHY AND DISEASED OIL PALM TREES. Journal of Oil Palm Research, 35(3), 517–527. https://doi.org/10.21894/jopr.2022.0068
Padhan, B. K., Sathee, L., Kumar, S., Chinnusamy, V., Krishnan, S. G., & Kumar, A. (2023). Nitrogen dose dependent changes in leaf greenness, crop phenology, grain nitrogen content and yield in rice (Oryza sativa L.) sub-species. Indian Journal of Genetics and Plant Breeding, 83(2), 176–184. https://doi.org/10.31742/ISGPB.83.2.3
Park, J.-R., Jang, Y.-H., Kim, E.-G., Lee, G.-S., & Kim, K.-M. (2023). Nitrogen Fertilization Causes Changes in Agricultural Characteristics and Gas Emissions in Rice Field. Sustainability, 15(4), 3336. https://doi.org/10.3390/su15043336
Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V. R., Murayama, Y., & Ranagalage, M. (2020). Sentinel-2 data for land cover/use mapping: A review. Remote Sensing, 12(14), 1–35. https://doi.org/10.3390/rs12142291
Phyu, P., Islam, M. R., Sta Cruz, P. C., Collard, B. C. Y., & Kato, Y. (2020). Use of NDVI for indirect selection of high yield in tropical rice breeding. Euphytica, 216(5). https://doi.org/10.1007/s10681-020-02598-7
Pratiwi, D. (2022). Analysis of Indonesia national rice availability towards self-support with a dynamic model approach. Economic Management and Social Sciences Journal, 1(3), 91–101. https://doi.org/10.56787/ecomans.v1i3.15
Qi, J., Jiang, J., Zhou, K., Xie, D., & Huang, H. (2023). Fast and accurate simulation of canopy reflectance under wavelength-dependent optical properties using a semi-empirical 3D radiative transfer model. Journal of Remote Sensing, 3, 1–12. https://doi.org/10.34133/remotesensing.0017
Qiu, H., Yang, S., Jiang, Z., Xu, Y., & Jiao, X. (2022). Effect of irrigation and fertilizer management on rice yield and nitrogen loss: a meta-analysis. Plants, 11(13), 1–16. https://doi.org/10.3390/plants11131690
Radočaj, D., Šiljeg, A., Marinović, R., & Jurišić, M. (2023). State of Major Vegetation Indices in Precision Agriculture Studies Indexed in Web of Science: A Review. Agriculture (Switzerland), 13(3), 1–16. https://doi.org/10.3390/agriculture13030707
Rehman, T. H., Lundy, M. E., & Linquist, B. A. (2022). Comparative sensitivity of vegetation indices measured via proximal and aerial sensors for assessing N status and predicting grain yield in rice cropping systems. Remote Sensing, 14(12), 1–18. https://doi.org/10.3390/rs14122770
Sharifi, A. (2020). Using sentinel-2 data to predict nitrogen uptake in maize crop. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2656–2662. https://doi.org/10.1109/JSTARS.2020.2998638
Somvanshi, S. S., & Kumari, M. (2020). Comparative analysis of different vegetation indices with respect to atmospheric particulate pollution using sentinel data. Applied Computing and Geosciences, 7, 1–10. https://doi.org/10.1016/j.acags.2020.100032
Stamford, J. D., Vialet-Chabrand, S., Cameron, I., & Lawson, T. (2023). Development of an accurate low cost NDVI imaging system for assessing plant health. Plant Methods, 19(1), 1–19. https://doi.org/10.1186/s13007-023-00981-8
Sugianto, Rusdi, M., Budi, M., Farhan, A., & Akhyar. (2023). Agricultural droughts monitoring of Aceh Besar Regency Rice Production Center, Aceh, Indonesia – application vegetation conditions index using Sentinel-2 image data. Journal of Ecological Engineering, 24(1), 159–171. https://doi.org/10.12911/22998993/155999
Tiruneh, G. A., Meshesha, D. T., Adgo, E., Tsunekawa, A., Haregeweyn, N., Fenta, A. A., & Reichert, J. M. (2022). A leaf reflectance-based crop yield modeling in Northwest Ethiopia. Plos One, 17(6), 1–21. https://doi.org/10.1371/journal.pone.0269791
Urmi, A. T., Rahman, Md. M., Islam, Md. M., Islam, Md. A., Jahan, A. N., Mia, Md. A. B., … Kalaji, H. M. (2022). Integrated nutrient management for rice yield, soil fertility, and carbon sequestration. Plants, 11(1), 2–17. https://doi.org/https://doi.org/10.3390/plants11010138
Vitasari, W., Daniel, & Munir, A. (2017). Pendugaan Produksi Dan Indeks Vegetasi Tanaman Padi Menggunakan Data Citra Platform Unmanned Aerial Vehicle (UAV) Dan Data Citra Satelit Landsat 8. Jurnal AgriTechno, 10(2), 203–216. Retrieved from https://doi.org/10.20956/at.v10i2.72
Waleed, M., Mubeen, M., Ahmad, A., Habib-ur-Rahman, M., Amin, A., Farid, H. U., … EL Sabagh, A. (2022). Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation. Scientific Reports, 12(1), 1–15. https://doi.org/10.1038/s41598-022-17454-y
Wang, Y., Li, Y., Xie, Y., Yang, X., He, Z., Tian, H., … Pan, S. (2023). Effects of nitrogen fertilizer rate under deep placement on grain yield and nitrogen use efficiency in mechanical pot-seedling transplanting tice. Journal of Plant Growth Regulation, 42(5), 3100–3110. https://doi.org/10.1007/s00344-022-10773-4
Wright, D. L., Rasmussen, V. P., & Ramsey, R. D. (2005). Comparing the use of remote sensing with traditional techniques to detect nitrogen stress in wheat. Geocarto International, 20(1), 63–68. https://doi.org/10.1080/10106040508542337
Xu, S., Xu, X., Blacker, C., Gaulton, R., Zhu, Q., Yang, M., … Chen, L. (2023). Estimation of leaf nitrogen content in rice using vegetation indices and feature variable optimization with information fusion of multiple-sensor images from UAV. Remote Sensing, 15(3), 1–24. https://doi.org/10.3390/rs15030854
DOI: https://doi.org/10.22146/ijg.87159
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- Chlorophyll, Nitrogen and Yield Estimation on Rice Using Sentinel 2A
- Estimating the contents of Chlorophyll, Nitrogen, and Yields on Rice through Sentinel-2 Vegetation Indices in Heterogeneous Land Management
- Estimating the contents of Chlorophyll, Nitrogen, and Yields on Rice through Sentinel-2 Vegetation Indices
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